In some conventional receivers, improvements may require extensive system modifications that may be very costly and, in some cases, may even be impractical. Determining the right approach to achieve design improvements may depend on the optimization of a receiver system to a particular modulation type and/or to the various kinds of noises that may be introduced by a transmission channel. For example, receiver optimization for wireless systems such as global system for mobile communication (GSM) and general packet radio systems (GPRS) may consider the characteristics of Gaussian minimum shift keying (GMSK) modulation, while receiver optimization for enhanced data rate for GSM evolution (EDGE) systems may consider the characteristics of eight phase shift keying (8-PSK) modulation. In this regard, consideration may be given to, for example, the fact that EDGE system receivers may need to provide three times higher nominal bit rates than GSM receivers systems as 8-PSK modulation transmits three bits-per-symbol in contrast to one bit-per-symbol for GMSK modulation. Improvements in the design and implementation of optimized receivers for decoding convolutional encoded data may require modifications to the various algorithms used by these receivers.
Optimization of a receiver system may also be based on whether the signals being received, generally in the form of successive symbols or information bits, are interdependent. Signals received from, for example, a convolutional encoder, may be interdependent signals, that is, signals with memory. In this regard, a convolutional encoder may generate NRZI or continuous-phase modulation (CPM), which is generally based on a finite state machine operation.
One method or algorithm for signal detection in a receiver system that decodes convolutional encoded data is maximum-likelihood sequence detection or estimation (MLSE). The MLSE is an algorithm that performs soft decisions while searching for a sequence that minimizes a distance metric in a trellis that characterizes the memory or interdependence of the transmitted signal. In this regard, an operation based on the Viterbi algorithm may be utilized to reduce the number of sequences in the trellis search when new signals are received.
Another method or algorithm for signal detection of convolutional encoded data that makes symbol-by-symbol decisions is maximum a posteriori probability (MAP). The optimization of the MAP algorithm is based on minimizing the probability of a symbol error. In many instances, the MAP algorithm may be difficult to implement because of its computational complexity.
Further limitations and disadvantages of conventional and traditional approaches will become apparent to one of skill in the art, through comparison of such systems with some aspects of the present invention as set forth in the remainder of the present application with reference to the drawings.